Predictive value of systemic immune-inflammation index in recurrent urethral strictures

Özsoy, Emrah; Kutluhan, Musab Ali; Tokuç, Emre; Artuk, İlker; Kayar, Rıdvan; Akyüz, Mehmet; Öztürk, Metin İshak

doi:10.1177/03915603221132033

Cited by 5 publications

(4 citation statements)

References 16 publications

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“…A recent investigation found higher SII levels in recurrent urethral stricture cases, with a reported sensitivity of 59.62% and specificity of 70.41% [ 16 ]. However, our study observed no statistically significant differences in PLR, NLR, SII, MI, DRR, and SIRI values between the two groups.…”

Section: Discussionmentioning

confidence: 99%

Machine learning algorithm predicts urethral stricture following transurethral prostate resection

Altıntaş,

Şahin,

Babayev

et al. 2024

World J Urol

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Purpose To predict the post transurethral prostate resection(TURP) urethral stricture probability by applying different machine learning algorithms using the data obtained from preoperative blood parameters. Methods A retrospective analysis of data from patients who underwent bipolar-TURP encompassing patient characteristics, preoperative routine blood test outcomes, and post-surgery uroflowmetry were used to develop and educate machine learning models. Various metrics, such as F1 score, model accuracy, negative predictive value, positive predictive value, sensitivity, specificity, Youden Index, ROC AUC value, and confidence interval for each model, were used to assess the predictive performance of machine learning models for urethral stricture development. Results A total of 109 patients’ data (55 patients without urethral stricture and 54 patients with urethral stricture) were included in the study after implementing strict inclusion and exclusion criteria. The preoperative Platelet Distribution Width, Mean Platelet Volume, Plateletcrit, Activated Partial Thromboplastin Time, and Prothrombin Time values were statistically meaningful between the two cohorts. After applying the data to the machine learning systems, the accuracy prediction scores for the diverse algorithms were as follows: decision trees (0.82), logistic regression (0.82), random forests (0.91), support vector machines (0.86), K-nearest neighbors (0.82), and naïve Bayes (0.77). Conclusion Our machine learning models’ accuracy in predicting the post-TURP urethral stricture probability has demonstrated significant success. Exploring prospective studies that integrate supplementary variables has the potential to enhance the precision and accuracy of machine learning models, consequently progressing their ability to predict post-TURP urethral stricture risk.

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Section: Discussionmentioning

confidence: 99%

Machine learning algorithm predicts urethral stricture following transurethral prostate resection

Altıntaş,

Şahin,

Babayev

et al. 2024

World J Urol

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“…On the other hand, hematological inflammatory parameters predicting post-treatment outcomes in urethroplasty has also been a topic of debate [ 11 14 ]. Some of these studies lack advanced statistics, and some emphasize on specific parameters.…”

Section: Discussionmentioning

confidence: 99%

“…In this study, we propose another potential biomarker, which is PIV, on predicting urethroplasty outcomes, both to add a novel “player” to the “game” and to enhance the creation of potential models and nomograms. Özsoy et al [ 11 ] has stated that SII with a cut-off value of 252 has the potential to predict failure after DVIU, with an overall risk of about 3.5 fold. There are conflicting results about NLR.…”

Section: Discussionmentioning

confidence: 99%

“…Stricture length and location, patients’ ages, patient comorbidities such as the presence of obesity or diabetes mellitus, etiology of stricture, presence of previous urethral surgery (direct visual internal urethrotomy [DVIU] or open urethroplasty) or dilatation history, urethroplasty technique or the duration until recurrence have been proposed and discussed thoroughly for projecting the prognosis patients [ 3 7 8 9 10 ]. Lately, based on spongiofibrosis being an end-result of a chronic inflammatory process of the urethral tissue, hematologic profiles of the patients such as neutrophil-lymphocyte ratio (NLR), platelet-lymphocyte ratio (PLR), and systemic immune-inflammation index (SII) were presented in aim to foresee recurrence [ 11 12 13 14 ]. Besides from these hematologic inflammatory indexes, pan-immune-inflammation value (PIV) has been proposed as a biomarker in various cancer prognoses or in chronic inflammatory/infectious diseases such as membranous nephropathy, lupus nephritis and Fournier’s gangrene [ 15 16 17 ].…”

Section: Introductionmentioning

confidence: 99%

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Inflammation indexes and machine-learning algorithm in predicting urethroplasty success

Tokuc,

Eksi,

Kayar

et al. 2024

Investig Clin Urol

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Purpose To assess the predictive capability of hematological inflammatory markers for urethral stricture recurrence after primary urethroplasty and to compare traditional statistical methods with a machine-learning-based artificial intelligence algorithm. Materials and Methods Two hundred eighty-seven patients who underwent primary urethroplasty were scanned. Ages, smoking status, comorbidities, hematological inflammatory parameters (neutrophil-lymphocyte ratios, platelet-lymphocyte ratios [PLR], systemic immune-inflammation indexes [SII], and pan-immune-inflammation values [PIV]), stricture characteristics, history of previous direct-visual internal urethrotomy, urethroplasty techniques, and grafts/flaps placements were collected. Patients were followed up for one year for recurrence and grouped accordingly. Univariate and multivariate logistic regression analyses were conducted to create a predictive model. Additionally, a machine-learning-based logistic regression analysis was implemented to compare predictive performances. p<0.05 was considered statistically significant. Results Comparative analysis between the groups revealed statistically significant differences in stricture length (p=0.003), localization (p=0.027), lymphocyte counts (p=0.008), PLR (p=0.003), SII (p=0.003), and PIV (p=0.001). In multivariate analysis, stricture length (odds ratio [OR] 1.230, 95% confidence interval [CI] 1.142–1.539, p<0.0001) and PIV (OR 1.002, 95% CI 1.000–1.003, p=0.039) were identified as significant predictors of recurrence. Classical logistic regression model exhibited a sensitivity of 0.76, specificity of 0.43 with an area under curve (AUC) of 0.65. However, the machine-learning algorithm outperformed traditional methods achieving a sensitivity of 0.80, specificity of 0.76 with a higher AUC of 0.82. Conclusions PIV and machine-learning algorithms shows promise on predicting urethroplasty outcomes, potentially leading to develop possible nomograms. Evolving machine-learning algorithms will contribute to more personalized and accurate approaches in managing urethral stricture.

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Is there an association between a high monocyte/high-density lipoprotein cholesterol ratio and recurrence of urethral stricture after internal urethrotomy?

Ozcan,

Polat,

Baran

et al. 2024

The French Journal of Urology

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Predictive value of systemic immune-inflammation index in recurrent urethral strictures

Cited by 5 publications

References 16 publications

Machine learning algorithm predicts urethral stricture following transurethral prostate resection

Machine learning algorithm predicts urethral stricture following transurethral prostate resection

Inflammation indexes and machine-learning algorithm in predicting urethroplasty success

Is there an association between a high monocyte/high-density lipoprotein cholesterol ratio and recurrence of urethral stricture after internal urethrotomy?

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